REVIGIS Informe resumido

This study proposes a segmentation procedure based on grey-level and multivariate texture to extract spatial objects from an image scene. Object uncertainty is quantified to identify transitions zones of objects with indeterminate boundaries. The Local Binary Pattern (LBP) operator, modelling texture, is integrated into a hierarchical splitting segmentation to identify homogeneous texture regions in an image. We propose a multivariate extension of the standard univariate LBP operator to describe colour texture.

The paper is illustrated with two case studies. The first considers an image with a composite of five textures regions. The two LBP operators provide good segmentation results. The second case study involves segmentation of coastal landform and land cover objects using a LiDAR DEM and multi-spectral CASI image of a coastal area in the UK. The multivariate LBP operator performs better than the univariate LBP operator, segmenting the area into meaningful objects, yielding valuable information on uncertainty at the transition zones. We conclude that the multivariate LBP operator is a meaningful extension to standard texture classifiers.